Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "200" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 54 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 52 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459869 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459868 | RF_maintenance | 100.00% | 100.00% | 74.38% | 0.00% | - | - | 23.537277 | 63.230600 | 28.252874 | 29.551919 | 10.704571 | 14.651193 | 5.037306 | 12.687675 | 0.0479 | 0.1912 | 0.1198 | nan | nan |
| 2459867 | RF_maintenance | 100.00% | 100.00% | 59.08% | 0.00% | - | - | 16.432612 | 46.141239 | 21.843513 | 23.360242 | 5.684970 | 9.838308 | 3.350580 | 12.874569 | 0.0473 | 0.2029 | 0.1322 | nan | nan |
| 2459866 | RF_maintenance | 100.00% | 100.00% | 55.96% | 0.00% | - | - | 18.951152 | 51.502248 | 21.023741 | 21.382744 | 6.183698 | 11.158294 | 1.094889 | 5.395369 | 0.0513 | 0.2075 | 0.1313 | nan | nan |
| 2459865 | RF_maintenance | 100.00% | 100.00% | 54.51% | 0.00% | - | - | 21.062411 | 58.669921 | 23.397008 | 28.492759 | 15.471161 | 19.712014 | 15.118092 | 16.639725 | 0.0451 | 0.2236 | 0.1405 | nan | nan |
| 2459864 | RF_maintenance | 100.00% | 100.00% | 73.79% | 0.00% | - | - | 25.507938 | 71.226652 | 7.511135 | 13.500627 | 8.122205 | 10.974662 | 5.375575 | 27.606754 | 0.0465 | 0.1924 | 0.1295 | nan | nan |
| 2459863 | RF_maintenance | 100.00% | 100.00% | 69.07% | 0.00% | - | - | 15.490109 | 46.404152 | 2.423006 | 0.876265 | 2.912886 | 6.158194 | 2.485027 | 13.704268 | 0.0467 | 0.1984 | 0.1304 | nan | nan |
| 2459862 | RF_maintenance | 100.00% | 100.00% | 67.52% | 0.00% | - | - | 15.363819 | 49.723736 | 8.122559 | 16.096472 | 12.486635 | 18.591203 | 1.810810 | 7.772214 | 0.0492 | 0.1988 | 0.1215 | nan | nan |
| 2459861 | RF_maintenance | 100.00% | 100.00% | 68.64% | 0.00% | - | - | 11.802616 | 35.123671 | 2.687213 | -0.182523 | 2.499090 | 2.793679 | 1.984077 | 9.980680 | 0.0465 | 0.1965 | 0.1160 | nan | nan |
| 2459860 | RF_maintenance | 100.00% | 100.00% | 57.04% | 0.00% | - | - | 12.811866 | 36.233776 | 7.908440 | 12.745242 | 14.554448 | 16.236271 | 1.981036 | 5.325468 | 0.0458 | 0.2028 | 0.1341 | nan | nan |
| 2459859 | RF_maintenance | 100.00% | 100.00% | 44.68% | 0.00% | - | - | 10.733740 | 32.545831 | 3.151592 | -0.172601 | 2.148930 | 2.173343 | 0.920736 | 2.627577 | 0.0482 | 0.2106 | 0.1377 | nan | nan |
| 2459858 | RF_maintenance | 100.00% | 100.00% | 95.11% | 0.00% | 100.00% | 0.00% | 11.677385 | 34.724317 | 3.163283 | -0.482674 | 2.185114 | 3.410037 | 1.970158 | 7.542657 | 0.0479 | 0.2038 | 0.1332 | 1.265237 | 1.987776 |
| 2459857 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 5.046590 | 4.074326 | 0.582308 | 2.662401 | 2.083613 | -0.461563 | 4.960186 | 6.433571 | 0.0668 | 0.0423 | 0.0215 | nan | nan |
| 2459856 | RF_maintenance | 100.00% | 100.00% | 99.43% | 0.00% | 100.00% | 0.00% | 17.307219 | 49.628673 | 7.796184 | 11.550974 | 6.446090 | 11.190583 | 2.620625 | -1.809000 | 0.0480 | 0.2006 | 0.1339 | 1.212078 | 1.583630 |
| 2459855 | RF_maintenance | 100.00% | 100.00% | 97.32% | 0.00% | 100.00% | 0.00% | 18.219379 | 55.090675 | 7.209504 | 13.434380 | 2.809717 | 6.060465 | 1.064928 | -1.114998 | 0.0485 | 0.2160 | 0.1482 | 1.189857 | 1.511832 |
| 2459854 | RF_maintenance | 100.00% | 100.00% | 94.00% | 0.00% | 100.00% | 0.00% | 18.256309 | 55.377525 | 5.169951 | 11.727445 | 4.013889 | 5.598671 | 4.390431 | 0.949686 | 0.0498 | 0.2366 | 0.1673 | 1.204393 | 1.508662 |
| 2459853 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 15.121084 | 41.119658 | 7.838391 | 14.531188 | 6.832286 | 9.295279 | 3.147642 | -1.398086 | 0.0504 | 0.2079 | 0.1349 | 1.224642 | 1.640459 |
| 2459852 | RF_maintenance | 100.00% | 100.00% | 55.68% | 0.00% | 100.00% | 0.00% | 13.721196 | 42.676192 | 8.503694 | 13.325663 | 15.481006 | 15.505904 | 16.811791 | 14.270269 | 0.0458 | 0.4044 | 0.3168 | 1.205998 | 2.001778 |
| 2459851 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | RF_maintenance | 100.00% | 100.00% | 82.94% | 0.00% | 100.00% | 0.00% | 14.468261 | 46.833015 | 6.900012 | 13.096907 | 10.212518 | 20.154058 | 6.489883 | 7.813720 | 0.0489 | 0.3204 | 0.2208 | 1.213461 | 1.558048 |
| 2459849 | RF_maintenance | 100.00% | 100.00% | 84.68% | 0.00% | 100.00% | 0.00% | 16.792554 | 48.454147 | 15.330846 | 26.898059 | 7.011023 | 13.551986 | 3.477155 | 0.870312 | 0.0488 | 0.2980 | 0.2032 | 1.226015 | 1.592434 |
| 2459848 | RF_maintenance | 100.00% | 100.00% | 83.79% | 0.00% | 100.00% | 0.00% | 15.467711 | 44.575018 | 7.769536 | 19.075644 | 14.294638 | 22.875158 | 1.632348 | 0.204478 | 0.0487 | 0.2934 | 0.2022 | 1.199897 | 1.497481 |
| 2459847 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.692049 | 46.477765 | 6.941161 | 18.217546 | 21.969969 | 16.764847 | 0.405474 | -0.581668 | 0.0460 | 0.1924 | 0.1236 | 1.208910 | 1.564897 |
| 2459841 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 63.230600 | 23.537277 | 63.230600 | 28.252874 | 29.551919 | 10.704571 | 14.651193 | 5.037306 | 12.687675 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 46.141239 | 16.432612 | 46.141239 | 21.843513 | 23.360242 | 5.684970 | 9.838308 | 3.350580 | 12.874569 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 51.502248 | 51.502248 | 18.951152 | 21.382744 | 21.023741 | 11.158294 | 6.183698 | 5.395369 | 1.094889 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 58.669921 | 21.062411 | 58.669921 | 23.397008 | 28.492759 | 15.471161 | 19.712014 | 15.118092 | 16.639725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 71.226652 | 71.226652 | 25.507938 | 13.500627 | 7.511135 | 10.974662 | 8.122205 | 27.606754 | 5.375575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 46.404152 | 15.490109 | 46.404152 | 2.423006 | 0.876265 | 2.912886 | 6.158194 | 2.485027 | 13.704268 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 49.723736 | 15.363819 | 49.723736 | 8.122559 | 16.096472 | 12.486635 | 18.591203 | 1.810810 | 7.772214 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 35.123671 | 35.123671 | 11.802616 | -0.182523 | 2.687213 | 2.793679 | 2.499090 | 9.980680 | 1.984077 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 36.233776 | 12.811866 | 36.233776 | 7.908440 | 12.745242 | 14.554448 | 16.236271 | 1.981036 | 5.325468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 32.545831 | 10.733740 | 32.545831 | 3.151592 | -0.172601 | 2.148930 | 2.173343 | 0.920736 | 2.627577 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 34.724317 | 34.724317 | 11.677385 | -0.482674 | 3.163283 | 3.410037 | 2.185114 | 7.542657 | 1.970158 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Temporal Discontinuties | 6.433571 | 4.074326 | 5.046590 | 2.662401 | 0.582308 | -0.461563 | 2.083613 | 6.433571 | 4.960186 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 49.628673 | 17.307219 | 49.628673 | 7.796184 | 11.550974 | 6.446090 | 11.190583 | 2.620625 | -1.809000 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 55.090675 | 55.090675 | 18.219379 | 13.434380 | 7.209504 | 6.060465 | 2.809717 | -1.114998 | 1.064928 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 55.377525 | 55.377525 | 18.256309 | 11.727445 | 5.169951 | 5.598671 | 4.013889 | 0.949686 | 4.390431 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 41.119658 | 41.119658 | 15.121084 | 14.531188 | 7.838391 | 9.295279 | 6.832286 | -1.398086 | 3.147642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 42.676192 | 13.721196 | 42.676192 | 8.503694 | 13.325663 | 15.481006 | 15.505904 | 16.811791 | 14.270269 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 46.833015 | 14.468261 | 46.833015 | 6.900012 | 13.096907 | 10.212518 | 20.154058 | 6.489883 | 7.813720 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 48.454147 | 16.792554 | 48.454147 | 15.330846 | 26.898059 | 7.011023 | 13.551986 | 3.477155 | 0.870312 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 44.575018 | 44.575018 | 15.467711 | 19.075644 | 7.769536 | 22.875158 | 14.294638 | 0.204478 | 1.632348 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 46.477765 | 46.477765 | 17.692049 | 18.217546 | 6.941161 | 16.764847 | 21.969969 | -0.581668 | 0.405474 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |